Temporary workspace assignment

Abstract

There are provided a system, a method and a computer program product for assigning a workspace. The system receives one or more reservation request for the workspace, associated with one or more facilities including one or more workspace areas. Each workspace area includes one or more workspaces. The system receives inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities. The system predicts an energy cost for each workspace area. The system determines a user desirability value for the each workspace area. The system determines a minimum cost for operating the one or more facilities, which satisfies the received reservation request. The system selects one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request. The selected workspace area has maximum user desirability values.

What is claimed is: 
     
       1. A method for assigning a workspace, the method comprising:
 receiving, at a hardware processor, one or more reservation requests for the workspace associated with one or more facilities including one or more workspace areas, each workspace area including one or more workspaces; 
 receiving, at the processor, from one or more databases, historical weather condition data, historical occupancy rates data and historical date data; 
 receiving, at the processor, inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities; 
 predicting, at the processor, based on the received inputs and a received reservation request, an energy cost for each workspace area, said predicting comprising:
 running, at the processor, a learning algorithm with the received inputs and the received reservation request in order to determine the energy cost of the each workspace area which corresponds to the received inputs and the received reservation request, wherein said running said learning algorithm comprises:
 running, at the processor, a supervised learning algorithm or a decision tree algorithm in order to find a match between the received inputs and the received reservation request and historical data which includes one or more of: the historical weather condition data, the historical occupancy rates data, the historical date data, and a historical reservation request; and 
 deriving, at the processor, based on the found match, the determined energy cost which corresponds to the matched historical data, and wherein the predicting the energy cost of the each workspace area further comprises: 
 determining, at the processor, based on the received inputs, the received reservation request, and the found matched historical data, the energy cost of the each workspace area when the each workspace area is occupied by at least one user or no user; 
 
 
 determining, at the processor, a user desirability value for the each workspace area; 
 determining, at the processor, based on the predicted energy cost and the determined user desirability value, a minimum cost for operating the one or more facilities, which satisfies the received reservation request; and 
 selecting one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request, the selected workspace area having maximum user desirability values, 
 wherein a processor connected to a memory device perform the receiving the reservation request, the receiving the inputs, the predicting, the determining the user desirability value, the determining the minimum cost, and the selecting. 
 
     
     
       2. The method according to  claim 1 , wherein the weather condition data represents conditions external to the one or more facilities, the conditions including one or more of: an outside temperature range and an amount of cloud coverage in a sky. 
     
     
       3. The method according to  claim 1 , wherein the received reservation request represents:
 an advanced reservation for workspaces and a total number of workspaces needed. 
 
     
     
       4. The method according to  claim 1 , wherein the date data includes:
 an amount of a shade made by one or more building, an angle of sunlight relative to a horizon, and a duration of sunlight. 
 
     
     
       5. The method according to  claim 4 , wherein determining the user desirability value further includes:
 receiving, from each user, a survey that includes a rating of comfortability, a rating of an access to amenities, and a rating of satisfaction of a workspace that the each user used. 
 
     
     
       6. A system for assigning a workspace, the system comprising:
 a memory device; 
 a processor connected to the memory device, 
 wherein the processor is configured to perform: 
 receiving one or more reservation requests for the workspace associated with one or more facilities including one or more workspace areas, each workspace area including one or more workspaces; 
 receiving, from one or more databases, historical weather condition data, historical occupancy rates data and historical date data; 
 receiving inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities; 
 predicting, based on the received inputs and a received reservation request, an energy cost for each workspace area, said predicting comprising:
 running a learning algorithm with the received inputs and the received reservation request in order to determine the energy cost of the each workspace area which corresponds to the received inputs and the received reservation request, wherein said running said learning algorithm comprises:
 running a supervised learning algorithm or a decision tree algorithm in order to find a match between the received inputs and the received reservation request and historical data which includes one or more of: the historical weather condition data, the historical occupancy rates data, the historical date data, and a historical reservation request; and 
 deriving based on the found match, the determined energy cost which corresponds to the matched historical data, and wherein the predicting the energy cost of the each workspace area further comprises: 
 determining based on the received inputs, the received reservation request, and the found matched historical data, the energy cost of the each workspace area when the each workspace area is occupied by at least one user or no user; 
 
 
 determining a user desirability value for the each workspace area; 
 determining, based on the predicted energy cost and the determined user desirability value, a minimum cost for operating the one or more facilities, which satisfies the received reservation request; and 
 selecting one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request, the selected workspace area having maximum user desirability values. 
 
     
     
       7. The system according to  claim 6 , wherein the weather condition data represents conditions external to the one or more facilities, the conditions including one or more of: an outside temperature range and an amount of cloud coverage in a sky. 
     
     
       8. The system according to  claim 6 , wherein the received reservation request represents:
 an advanced reservation for workspaces and a total number of workspaces needed. 
 
     
     
       9. The system according to  claim 6 , wherein the date data:
 an amount of a shade made by one or more building, an angle of sunlight relative to a horizon, and a duration of sunlight. 
 
     
     
       10. The system according to  claim 9 , wherein in order to perform determining the user desirability value, the processor is further configured to perform:
 receiving, from each user, a survey that includes a rating of comfortability, a rating of an access to amenities, and a rating of satisfaction of a workspace that the each user used. 
 
     
     
       11. A computer program product for assigning a workspace, the computer program product comprising a non-transitory computer readable storage medium, the computer readable storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, the method comprising:
 receiving one or more reservation requests for the workspace associated with one or more facilities including one or more workspace areas, each workspace area including one or more workspaces; 
 receiving, from one or more databases, historical weather condition data, historical occupancy rates data and historical date data; 
 receiving inputs including one or more of: weather condition data, occupancy rates data, and date data, associated with the one or more facilities; 
 predicting, based on the received inputs and a received reservation request, an energy cost for each workspace area, said predicting comprising:
 running a learning algorithm with the received inputs and the received reservation request in order to determine the energy cost of the each workspace area which corresponds to the received inputs and the received reservation request, wherein said running said learning algorithm comprises:
 running a supervised learning algorithm or a decision tree algorithm in order to find a match between the received inputs and the received reservation request and historical data which includes one or more of: the historical weather condition data, the historical occupancy rates data, the historical date data, and a historical reservation request; and 
 deriving based on the found match, the determined energy cost which corresponds to the matched historical data, and wherein the predicting the energy cost of the each workspace area further comprises: 
 determining based on the received inputs, the received reservation request, and the found matched historical data, the energy cost of the each workspace area when the each workspace area is occupied by at least one user or no user; and 
 
 
 determining a user desirability value for the each workspace area; 
 determining, based on the predicted energy cost and the determined user desirability value, a minimum cost for operating the one or more facilities, which satisfies the received reservation request; and 
 selecting one or more workspace area in the one or more facilities according to the determined minimum cost and the received reservation request, the selected workspace area having maximum user desirability values.